About Keras Layers

Overview

Keras layers are the fundamental building block of keras models. Layers are created using a wide variety of layer_ functions and are typically composed together by stacking calls to them using the pipe %>% operator. For example:

Properties

layer$input_spec — List of input specifications. Each entry describes one required input: (ndim, dtype). A layer with n input tensors must have an input_spec of length n.

layer$trainable — Boolean, whether the layer weights will be updated during training.

layer$uses_learning_phase – Whether any operation of the layer uses K.in_training_phase() or K.in_test_phase().

layer$input_shape — Input shape. Provided for convenience, but note that there may be cases in which this
attribute is ill-defined (e.g. a shared layer with multiple input shapes), in which case
requesting input_shape will result in an error. Prefer using get_input_shape_at(layer, node_index).

layer$output_shape — Output shape. See above.

layer$inbound_nodes — List of nodes.

layer$outbound_nodes — List of nodes.

layer$input, layer$output — Input/output tensor(s). Note that if the layer is used more than
once (shared layer), this is ill-defined and will result in an error. In such cases, use get_input_at(layer, node_index).

layer$input_mask, layer$output_mask — Same as above, for masks.

layer$trainable_weights — List of variables.

layer$non_trainable_weights — List of variables.

layer$weights — The concatenation of the lists trainable_weights and
non_trainable_weights (in this order).